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Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation

This study examines the status of nonmodal phonation (e.g. breathy and creaky voice) in British English using smartphone recordings from over 2,500 speakers. With this novel data collection method, it uncovers effects that have not been reported in past work, such as a relationship between speakers’...

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Detalles Bibliográficos
Autores principales: Gittelson, Ben, Leemann, Adrian, Tomaschek, Fabian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861257/
https://www.ncbi.nlm.nih.gov/pubmed/33733211
http://dx.doi.org/10.3389/frai.2020.565682
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author Gittelson, Ben
Leemann, Adrian
Tomaschek, Fabian
author_facet Gittelson, Ben
Leemann, Adrian
Tomaschek, Fabian
author_sort Gittelson, Ben
collection PubMed
description This study examines the status of nonmodal phonation (e.g. breathy and creaky voice) in British English using smartphone recordings from over 2,500 speakers. With this novel data collection method, it uncovers effects that have not been reported in past work, such as a relationship between speakers’ education and their production of nonmodal phonation. The results also confirm that previous findings on nonmodal phonation, including the greater use of creaky voice by male speakers than female speakers, extend to a much larger and more diverse sample than has been considered previously. This confirmation supports the validity of using crowd-sourced data for phonetic analyses. The acoustic correlates that were examined include fundamental frequency, H1*-H2*, cepstral peak prominence, and harmonic-to-noise ratio.
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spelling pubmed-78612572021-03-16 Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation Gittelson, Ben Leemann, Adrian Tomaschek, Fabian Front Artif Intell Artificial Intelligence This study examines the status of nonmodal phonation (e.g. breathy and creaky voice) in British English using smartphone recordings from over 2,500 speakers. With this novel data collection method, it uncovers effects that have not been reported in past work, such as a relationship between speakers’ education and their production of nonmodal phonation. The results also confirm that previous findings on nonmodal phonation, including the greater use of creaky voice by male speakers than female speakers, extend to a much larger and more diverse sample than has been considered previously. This confirmation supports the validity of using crowd-sourced data for phonetic analyses. The acoustic correlates that were examined include fundamental frequency, H1*-H2*, cepstral peak prominence, and harmonic-to-noise ratio. Frontiers Media S.A. 2021-01-25 /pmc/articles/PMC7861257/ /pubmed/33733211 http://dx.doi.org/10.3389/frai.2020.565682 Text en Copyright © 2021 Gittelson, Leemann and Tomaschek. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Gittelson, Ben
Leemann, Adrian
Tomaschek, Fabian
Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation
title Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation
title_full Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation
title_fullStr Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation
title_full_unstemmed Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation
title_short Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation
title_sort using crowd-sourced speech data to study socially constrained variation in nonmodal phonation
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861257/
https://www.ncbi.nlm.nih.gov/pubmed/33733211
http://dx.doi.org/10.3389/frai.2020.565682
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